Prognostic prediction and immunotherapy response analysis of the fatty acid metabolism-related genes in clear cell renal cell carcinoma

Identification of cellular senescence-associated genes as new biomarkers for predicting the prognosis and immunotherapy response of non-small cell lung cancer and construction of a prognostic model

Background

Globally, lung carcinoma remains the leading cause of death, with its associated morbidity and mortality rates remaining elevated. Despite the slow advancement of treatment, the outlook remains bleak. Cellular senescence represents a halt in the cell cycle, encompassing a range of physiological and pathological activities, along with diverse phenotypic alterations, including variations in secretory phenotype, macromolecular harm, and metabolic disturbances. Research has revealed its vital function in the formation and growth of tumors. This study aimed to examine cellular senescence-related mRNAs linked to the outlook of non-small cell lung cancer (NSCLC) and to formulate a predictive risk framework for NSCLC.

Methods

We acquired the NSCLC expression data from The Cancer Genome Atlas (TCGA) to examine mRNAs linked to cellular senescence. Both single-variable and multiple-variable cox proportion risk assessments were utilized to determine the traits of cellular senescence-related mRNAs linked to NSCLC prognosis. Subsequently, the prognostic model for cellular senescence-related mRNAs was integrated with clinical-pathological characteristics to create a prognostic nomogram. Furthermore, the study delved into the risk-oriented predictive model, examining immune infiltration and responses to immunotherapy among both high and low-risk categories.

Results

Utilizing both univariate and multivariate Cox proportion risk assessments, a risk model comprising 12 mRNAs associated with cellular aging was ultimately developed: IGFBP1, TLR3, WT1, ID1, PTTG1, ERRFI1, HEPACAM, MAP2K3, RAD21, NANOG, PRKCD, SOX5. Univariate analysis and multivariate analysis illustrated that the risk score served as a standalone indicator for prognosis, and the hazard ratio (HR) of the risk score were 1.182 (1.139-1.226) (p < 0.001) and 1.162 (1.119 - 1.206) (p < 0.001), respectively. Individual prognoses were forecasted using nomogram, c-index, and principal component analysis (PCA). Furthermore, the risk-oriented model revealed notable statistical variances in immune infiltration and response to immunotherapy among the high and low risk categories.

Conclusions

This study shows that mRNAs related to cell senescence associated with prognosis are reliable predictors of NSCLC immunotherapy reaction and prognosis.

Genomic nursing science revealed the prolyl 4-hydroxylase subunit alpha 2 as a significant biomarker involved in osteosarcoma

Backgrounds

This study aims to explore the clinical value of P4HA2 (prolyl 4-hydroxylase subunit alpha 2) in Osteosarcoma (OSC), and assess its potential to provide directions and clues for the practice of precision nursing.

Methods

The GSE73166 and GSE16088 datasets were used to explore the P4HA2 expression in OSC. We then used the clinical data of patients obtaining from TARGET database to assess the prognostic value of P4HA2 in OSC. We also evaluated the predictive value of prognostic model based on P4HA2-related genes. Further, GSEA analysis was performed to explore related pathways.

Results

The P4HA2 mRNA expression was higher in OSC than that in normal tissues and other bone cancer samples. Survival analysis found that P4HA2 high expression caused poor overall survival (OS) of patients with OSC and P4HA2 presented a favorable performance for predicting OS. Specifically, P4HA2 high expression statistically influenced the OS of patients with age≥15 years old and those with or without metastasis. Cox regression analysis indicated the independent prognostic value of P4HA2 in OSC, and nomogram analysis revealed its significant contribution to the survival probability of patients. We further established a prognostic model based on P4HA2-related genes, finding that prognostic model had a good prediction ability on OS. These results supported the clinical significance of P4HA2 in OSC. GSEA analysis suggested that P4HA2 was significantly related to the MAPK signaling pathway. In addition, P4HA2-associated natural killer cell-mediated cytotoxicity and T cell receptor signaling pathway were also predicted.

Conclusions

This study revealed that P4HA2 can serve as an important prognostic biomarker for OSC patients, and it may become a promising therapeutic target in OSC treatment.

P2RY13 is a prognostic biomarker and associated with immune infiltrates in renal clear cell carcinoma: A comprehensive bioinformatic study

Background and Aims

Clear cell renal cell carcinoma (ccRCC) is a common and aggressive form of cancer with a high incidence globally. This study aimed to investigate the role of P2RY13 in the progression of ccRCC and elucidate its mechanism of action.

Methods

Gene Expression Omnibus and The Cancer Genome Atlas databases were used to extract gene expression profiles of ccRCC. These profiles were annotated and visualized by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses, as well as Gene Set Enrichment Analysis (GSEA). The STRING database was used to establish a protein-protein interaction network and to analyze the functional similarity. The GEPIA2 database was used to predict survival associated with hub genes. Meanwhile, the TIMER2.0 database was used to assess immune cell infiltration and its link with the hub genes. Immunohistochemistry (IHC) was used to determine the difference between ccRCC and adjacent normal tissue.

Results

We identified 272 differentially expressed genes (DEGs). GO and KEGG analyses suggested that DEGs were primarily involved in lymphocyte activation, inflammatory response, immunological effector mechanism pathways. By cytohubba, the 20 highest-scoring hub genes were screened to identify critical genes in the protein-protein interaction network linked with ccRCC. Resting dendritic cells, CD8 T cells, and activated mast cells all showed a significant positive correlation with these hub genes. Moreover, a higher immune score was associated with increased prognostic risk scores, which in turn correlated with a poorer prognosis. IHC revealed that P2RY13 was expressed at higher levels in ccRCC compared to para-cancer tissues.

Conclusion

Identifying the DEGs will aid in the understanding of the causes and molecular mechanisms involved in ccRCC. P2RY13 may play a pivotal role in the progression and prognosis of ccRCC, potentially driving carcinogenesis though immune system mechanisms.